Soetanto, R and Proverbs, D G (2004) Intelligent models for predicting levels of client satisfaction. Journal of Construction Research, 5(2), pp. 233-253. ISSN 1609-9451
Abstract
This paper presents the development of artificial neural network models for predicting client satisfaction levels arising from the performance of contractors, based on data from a UK-wide questionnaire survey of clients. Important independent variables identified by the models indicate that long-term relationships may encourage higher satisfaction levels. Moreover, the performance of contractors was found to only partly contribute to determining levels of client satisfaction. Attributes of the assessor (i.e. client) were also found to be of importance, confirming that subjectivity is to some extent prevalent in performance assessment. The models demonstrate accurate and consistent predictive performance for "unseen" independent data. It is recommended that the models be used as a platform to develop an expert system aimed at advising project coalition (PC) participants on how to improve performance and enhance satisfaction levels. The use of this tool will ultimately help to create a performance-enhancing environment, leading to harmonious working relationships between PC participants.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural network; client satisfaction; contractor performance; performance assessment; project coalition |
| Index terms: | artificial neural network, survey, subjectivity, questionnaire, expert system, platform, satisfaction, contractor performance, independent variable, client satisfaction, performance assessment |
| Subjects: | data management, statistical analysis, data collection methods, modelling and simulation, project delivery, contract management, digital design, assessment methods, client relations, human factors and perception |
| Topics: | Project Management, Research Practice, Stakeholder Management, Digital Applications, Human Resources, Contract Administration |
| Descriptive scope: | 4 PCEA |
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